JPA 批量插入不会提高性能

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【中文标题】JPA 批量插入不会提高性能【英文标题】:JPA batch inserts does not improve performance 【发布时间】:2019-12-07 19:17:43 【问题描述】:

我想通过 JPA 批量插入来提高我的 postgresql 插入的性能。

我正在使用:

spring-boot-starter-data-jpa 2.1.3.RELEASE postgresql 42.2.5(jdbc 驱动程序)。 数据库为 PostgreSQL 9.6.2

我已经成功地激活了 JPA 的批量插入,但性能根本没有提高。

我在我的实体中使用@GeneratedValue(strategy = GenerationType.SEQUENCE)

我在我的 jdbc 连接字符串中使用 reWriteBatchedInserts=true

我设置了以下属性:
spring.jpa.properties.hibernate.jdbc.batch_size=100
spring.jpa.properties.hibernate.order_inserts=true
spring.jpa.properties.hibernate.generate_statistics=true
我使用 saveAll(collection) 方法 我尝试在每批后刷新和清理我的 entityManager 我尝试了 100 和 1000 的批次大小,每批次都刷新,没有明显变化。

我可以在日志中看到 hibernate 确实使用批量插入,但不确定我的数据库是否使用(我正在尝试获取日志,文件夹权限正在等待处理)。


@Service
@Configuration
@Transactional
public class SecteurGeographiqueServiceImpl implements SecteurGeographiqueService 

    private static final Logger logger = LoggerFactory.getLogger(SecteurGeographiqueServiceImpl.class);

@Value("$spring.jpa.properties.hibernate.jdbc.batch_size")
private int batchSize;

@PersistenceContext
private EntityManager entityManager;

@Autowired
private SecteurGeographiqueRepository secteurGeographiqueRepository;

    @Override
    public List<SecteurGeographique> saveAllSecteurGeographiquesISOs(List<SecteurGeographique> listSecteurGeographiques) 
    logger.warn("BATCH SIZE : " + this.batchSize);
    final List<SecteurGeographique> tempList = new ArrayList<>();
    final List<SecteurGeographique> savedList = new ArrayList<>();
    for (int i = 0; i < listSecteurGeographiques.size(); i++) 
        if ((i % this.batchSize) == 0) 
            savedList.addAll(this.secteurGeographiqueRepository.saveAll(tempList));
            tempList.clear();
            this.entityManager.flush();
            this.entityManager.clear();
        
        tempList.add(listSecteurGeographiques.get(i));
    
    savedList.addAll(this.secteurGeographiqueRepository.saveAll(tempList));
    tempList.clear();
    this.entityManager.flush();
    this.entityManager.clear();
    return savedList;
    



...

@Entity
public class SecteurGeographique 

    private static final long serialVersionUID = 1L;

    @Id
    @GeneratedValue(strategy = GenerationType.SEQUENCE)
    @Column(name = "id")
    public Long id;
...

我的存储库实现是:

org.springframework.data.jpa.repository.JpaRepository<SecteurGeographique, Long>

application.properties(连接部分):

spring.datasource.url=jdbc:postgresql://xx.xx.xx.xx:5432/bddname?reWriteBatchedInserts=true
spring.jpa.properties.hibernate.default_schema=schema
spring.datasource.username=xxxx
spring.datasource.password=xxxx
spring.datasource.driverClassName=org.postgresql.Driver
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.PostgreSQLDialect
spring.jpa.properties.hibernate.jdbc.lob.non_contextual_creation=true
spring.jpa.properties.hibernate.jdbc.batch_size=100
spring.jpa.properties.hibernate.order_inserts=true
spring.jpa.properties.hibernate.generate_statistics=true

在我的 16073 个实体插入后在日志中(此测试不包括刷新):

13:31:40.882 [restartedMain] INFO  o.h.e.i.StatisticalLoggingSessionEventListener - Session Metrics 
    15721506 nanoseconds spent acquiring 1 JDBC connections;
    0 nanoseconds spent releasing 0 JDBC connections;
    121091067 nanoseconds spent preparing 16074 JDBC statements;
    240144821872 nanoseconds spent executing 16073 JDBC statements;
    3778202166 nanoseconds spent executing 161 JDBC batches;
    0 nanoseconds spent performing 0 L2C puts;
    0 nanoseconds spent performing 0 L2C hits;
    0 nanoseconds spent performing 0 L2C misses;
    4012929596 nanoseconds spent executing 1 flushes (flushing a total of 16073 entities and 0 collections);
    0 nanoseconds spent executing 0 partial-flushes (flushing a total of 0 entities and 0 collections)

请注意,这只是一张表,没有约束/外键。只是将基本数据放在表格中,没什么特别的。

从日志看来确实有问题:

240144821872 nanoseconds spent executing <b>16073 JDBC statements</b>;
3778202166 nanoseconds spent executing 161 JDBC batches;

如果一切都在批处理中,不应该是“执行161 JDBC语句”吗?

使用刷新进行测试,批量大小为 100 然后 1000:

15:32:17.612 [restartedMain] WARN  f.g.j.a.r.s.i.SecteurGeographiqueServiceImpl - BATCH SIZE : 100
15:36:46.206 [restartedMain] INFO  o.h.e.i.StatisticalLoggingSessionEventListener - Session Metrics 
    15416324 nanoseconds spent acquiring 1 JDBC connections;
    0 nanoseconds spent releasing 0 JDBC connections;
    105369002 nanoseconds spent preparing 16234 JDBC statements;
    262388696401 nanoseconds spent executing 16073 JDBC statements;
    3669253410 nanoseconds spent executing 161 JDBC batches;
    0 nanoseconds spent performing 0 L2C puts;
    0 nanoseconds spent performing 0 L2C hits;
    0 nanoseconds spent performing 0 L2C misses;
    3956493726 nanoseconds spent executing 161 flushes (flushing a total of 16073 entities and 0 collections);
    0 nanoseconds spent executing 0 partial-flushes (flushing a total of 0 entities and 0 collections)


15:43:54.155 [restartedMain] WARN  f.g.j.a.r.s.i.SecteurGeographiqueServiceImpl - BATCH SIZE : 1000
15:48:22.335 [restartedMain] INFO  o.h.e.i.StatisticalLoggingSessionEventListener - Session Metrics 
    15676227 nanoseconds spent acquiring 1 JDBC connections;
    0 nanoseconds spent releasing 0 JDBC connections;
    111370586 nanoseconds spent preparing 16090 JDBC statements;
    265089247563 nanoseconds spent executing 16073 JDBC statements;
    599946208 nanoseconds spent executing 17 JDBC batches;
    0 nanoseconds spent performing 0 L2C puts;
    0 nanoseconds spent performing 0 L2C hits;
    0 nanoseconds spent performing 0 L2C misses;
    866452023 nanoseconds spent executing 17 flushes (flushing a total of 16073 entities and 0 collections);
    0 nanoseconds spent executing 0 partial-flushes (flushing a total of 0 entities and 0 collections)

每次我得到 4 分 30 秒的执行时间。批量插入感觉非常棒。 我错过了什么/误解了什么?

【问题讨论】:

你的仓库的实现是什么? @Zorglube 我使用 org.springframework.data.jpa.repository.JpaRepository。我相应地更新了问题。 如果您希望插入速度更快,尤其是 16073 插入,您最好使用本机参数化 SQL 查询:Insert Into SecteurGeographique (id, ...) Values( :id, ...) 如果你想走得更快一点,插入时获取SecteurGeographique_PK的位置来伪造你的id,插入后推SecteurGeographique_PK的新位置。 @Zorglube 是的,我了解这两个,但我的问题实际上是关于了解正在发生的事情。大多数使用我使用的解决方案的人都见证了非常显着的性能提升,而我没有。 【参考方案1】:

在 localhost (https://gareth.flowers/postgresql-portable/ v10.1.1) 上使用 postgresql 服务器尝试批量大小为 1000 后,执行运行不到 3 秒。所以看来代码或配置不应该归咎于此。

不幸的是,我无法调查为什么在远程 postgresql(托管在 AWS 上)上花费了这么多时间,但我只能得出结论,这是网络或数据库问题。

截至今天,我无法访问 postgresql 远程日志,但如果您对在 postgresql 实例上查找什么有任何建议,我会全力以赴。

带有批处理 (1000) 和 flush+clean 的日志:

16:20:52.360 [restartedMain] WARN  f.g.j.a.r.s.i.SecteurGeographiqueServiceImpl - BATCH SIZE : 1000
16:20:54.844 [restartedMain] INFO  o.h.e.i.StatisticalLoggingSessionEventListener - Session Metrics 
    523125 nanoseconds spent acquiring 1 JDBC connections;
    0 nanoseconds spent releasing 0 JDBC connections;
    44649191 nanoseconds spent preparing 16090 JDBC statements;
    1311557995 nanoseconds spent executing 16073 JDBC statements;
    204225325 nanoseconds spent executing 17 JDBC batches;
    0 nanoseconds spent performing 0 L2C puts;
    0 nanoseconds spent performing 0 L2C hits;
    0 nanoseconds spent performing 0 L2C misses;
    381230968 nanoseconds spent executing 17 flushes (flushing a total of 16073 entities and 0 collections);
    0 nanoseconds spent executing 0 partial-flushes (flushing a total of 0 entities and 0 collections)

没有批处理、刷新或清理的日志:

16:57:34.426 [restartedMain] INFO  o.h.e.i.StatisticalLoggingSessionEventListener - Session Metrics 
    725069 nanoseconds spent acquiring 1 JDBC connections;
    0 nanoseconds spent releasing 0 JDBC connections;
    55763008 nanoseconds spent preparing 32146 JDBC statements;
    2816525053 nanoseconds spent executing 32146 JDBC statements;
    0 nanoseconds spent executing 0 JDBC batches;
    0 nanoseconds spent performing 0 L2C puts;
    0 nanoseconds spent performing 0 L2C hits;
    0 nanoseconds spent performing 0 L2C misses;
    1796451447 nanoseconds spent executing 1 flushes (flushing a total of 16073 entities and 0 collections);
    0 nanoseconds spent executing 0 partial-flushes (flushing a total of 0 entities and 0 collections)

这个比较显示整个 JDBC 语句执行时间增加了 46%。

【讨论】:

注意不要把batch_size放得太高见这里:***.com/questions/14789052/…应该是

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